Predicting Locomotive Crew Performance in Rail Operations with Human and Automation Assistance

As new technologies are introduced into rail operations, models are needed to represent the task load of operators to identify periods of extreme workload that could be mitigated through technological interventions. To this end, a computational model is described to quantitatively simulate freight rail operator workload to understand the impacts of inserting intelligent automation on different crew configurations. A detailed task analysis served as the basis for identifying tasks performed during transit. Utilizing task characteristics and operating conditions as inputs, a discrete event simulation was designed to predict human operator workload. Results show that during heavy-traffic conditions, the presence of automation can impact the locomotive engineer performance more than the presence of a freight conductor in a short-haul freight rail setting. However, under typical conditions, assistance may not be as beneficial for human operator performance.